Abstract
Let us briefly recall the difficulty we want to overcome with the theoretical drug. The difficulty is that in the prototypical reaction defined by the Markov model
the rates may change under various mutations. One case that we have focused on in these notes is COmutations where the reaction rate from C to O is increased.
Keywords
 Theoretical Drug
 Closedblocked State
 Open Probability Density
 Wildtype Cases
 Dyad Concentration
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Let us briefly recall the difficulty we want to overcome with the theoretical drug. The difficulty is that in the prototypical reaction defined by the Markov model
the rates may change under various mutations. One case that we have focused on in these notes is COmutations where the reaction rate from C to O is increased. The reaction of a COmutation takes the form
where we assume that \(\mu \geqslant 1\) is a constant. We refer to this constant as the mutation severity index and the mutation is typically worse the larger the value of μ; furthermore, μ = 1 refers to the wild type case. Our aim is to devise a theoretical drug of the form
where the constants k _{ bc }, k _{ cb }, k _{ bo }, and k _{ ob } are used to tune the drug such that the effect of the mutation is reduced as much as possible. As above, we will consider blockers associated with the closed state, which means that k _{ ob } = 0, or blockers associated with the open state, which means that k _{ cb } = 0. The model and discretization parameters used throughout this chapter are given in Table 6.1.
6.1 Effect of the Mutation in the TwoDimensional Case
When the effect of the mutation is taken into account, the probability density functions are governed by the system
where we recall that the fluxes are given by
(see page 102). In Fig. 6.1, we compare the solution of this system when μ = 1 (wild type) and μ = 3 (mutant) and in Table 6.2 we give the statistics of the solutions. The total open probability increases from 0.430 for the wild type to 0.743 for the mutant. In addition, the expected concentrations of both the dyad and the junctional sarcoplasmic reticulum (JSR) decrease considerably. In the onedimensional (1D) case we observed that the variability of the solution decreased when the mutation was introduced. This observation seems to carry over to the twodimensional (2D) case.
6.2 A Closed State Drug
In the 1D case, we were able to compute a characterization of the closed state drug based on considering the equilibrium solution of the reaction scheme. Since the reaction scheme is the same in the 1D and 2D problems, we can use exactly the same characterization as above. Let us first recall that the reaction scheme of the closed state drug takes the form
We found above (see (3.9) on page 59) that the parameters of the closed state blocker should be related as
so the optimal value of k _{ bc } remains to be determined. To find the optimal value of this parameter, we need to extend the system (6.1) and (6.2) to account for the theoretical drug. When the closed state blocker is added, the steady state version of the probability density system reads
Our aim is now to compute the value of the single parameter k _{ bc } such that the open probability density function defined by the system (6.5)–(6.7) is as close as possible to the solution of the system (6.1) and (6.2) in the case of μ = 1 (i.e., the wild type case). In other words, we want to use the drug to repair the effect of the mutations in the sense that we want the open probability densities to be as close as possible to the wild type open probability densities.
In Fig. 6.2 we show the solution of the system (6.5)–(6.7) using μ = 3 and k _{ bc } = 0. 01, 0.1, 1, and 10 ms^{−1}. As expected, we note that the solution becomes increasingly similar to the wild type solution (see Fig. 6.1) as k _{ bc } increases.
6.2.1 Convergence as k _{ bc } Increases
Again we observe that the theoretical closed state blocker becomes more efficient for larger values of k _{ bc }. To obtain a more precise impression of the convergence, we compute the norm of the difference between the open probability of the wild type case and the open probability of the solution of the system (6.5)–(6.7) as a function of k _{ bc } using the norm defined by (2.40) on page 46. The result is shown in Fig. 6.3 and we again observe that, when k _{ bc } becomes sufficiently large, the effect of the mutation is repaired completely.
6.3 An Open State Drug
The reaction scheme of an open state blocker for a mutant is
We learned above that we had limited success in using the equilibrium solution to derive an optimal characterization of the open state drug. We will therefore directly optimize the two parameters k _{ bo } and k _{ ob }.
6.3.1 Probability Density Model for Open State Blockers in 2D
The probability density model in the presence of an open state drug is
In Fig. 6.4, we show the cost function defined by the norm (see (2.40) on page 56) of the difference between the open probability density function of the wild type (solution of (6.1) and (6.2) with μ = 1) and the open probability density function of the solution of the system (6.8)–(6.10) with μ = 3. By minimizing the cost function, using Matlab’s Fminsearch with default parameters and \(k_{ob} = k_{bo} = 1\) as an initial guess, we find that an optimal open state blocker is given by
6.3.1.1 Does the Optimal Theoretical Drug Change with the Severity of the Mutation?
One issue here is to see if the drug changes with the mutation severity index. Numerical experiments show that the optimal drug does change. In Fig. 6.5, we show the case in which μ = 10 and the optimum has shifted compared to Fig. 6.4.
6.4 Statistical Properties of the Open and Closed State Blockers in 2D
We introduced statistical properties of probability density functions in Sect. 4.2 (see page 74). In Sect. 4.6 (page 90), we observed that, for the 1D release problem, the closed state blocker completely repaired the statistical properties of the open state probability density functions. In addition, an optimized version of an open state blocker gave good results, but it was unable to repair the standard deviation of the open state probability density functions for the particular COmutations we considered.
The statistical properties of the solutions for 2D release are summarized in Table 6.3. The results are quite similar to the 1D case. Again, for the COmutations, the closed state blocker improves as the value of k _{ bc } increases and the optimized version of the open state blocker also provides good results.
6.5 Numerical Comparison of Optimal Open and Closed State Blockers
In the 1D case, we saw that for COmutations the closed state blocker was able to completely remove the effect of the mutation, whereas the open state blocker was less efficient. This result also holds in the 2D case. In Fig. 6.6, we compare the open probability density function of the steady state solution of the wild type (solution of (6.1) and (6.2) with μ = 1), the mutant (solution of (6.1) and (6.2) with μ = 3), the optimal closed state blocker (solution of (6.5)–(6.7) using μ = 3 and \(k_{bc} = 10\text{ ms}^{1})\) and the optimal open state blocker (solution of (6.8)–(6.10) with \(\mu = 3,k_{ob} = 0.3225\text{ ms}^{1},\) \(k_{bo} = 0.3346\text{ ms}^{1}).\) We observe that it is hard to see any difference between the open probability density function of the wild type and the mutant when the closed state blocker is applied. In addition, the optimal open state blocker improves the solution, but not as much as the closed state blocker does.
6.6 Stochastic Simulations in 2D Using Optimal Drugs
We have used the probability density approach to find an optimal closed state blocker. In Fig. 6.7 we show how the closed state blocker works in a dynamic simulation based on the scheme (5.11) and (5.12). We plot the concentrations of the wild type, the mutant (μ = 3), and the mutant when the closed state blocker is applied (\(k_{bc} = 10\text{ ms}^{1},\,k_{cb} = (\mu 1)k_{bc}\)). The dyad concentrations (x = x(t)) are on the lefthand side and the JSR concentrations (y = y(t)) are on the righthand side. As for the 1D simulations, we observe that the mutations significantly reduce the variability of the solutions and that this effect is basically completely repaired by the closed state blocker.
6.7 Notes

1.
The 2D stochastic differential equation and the associated probability density system is taken from Huertas and Smith [35].
References
M.A. Huertas, G.D. Smith, The dynamics of luminal depletion and the stochastic gating of Ca^{2+}activated Ca^{2+} channels and release sites. J. Theor. Biol. 246(2), 332–354 (2007)
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Tveito, A., Lines, G.T. (2016). Computing Theoretical Drugs in the TwoDimensional Case. In: Computing Characterizations of Drugs for Ion Channels and Receptors Using Markov Models. Lecture Notes in Computational Science and Engineering, vol 111. Springer, Cham. https://doi.org/10.1007/9783319300306_6
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